Removing Outliers

## [1] "Outliers : 135499aaw, 9l7s14ocz, 9l7s14ocz, g6m2iu73e, lpc2zjkex, srn0c21wi"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : at13n1mb2, srn0c21wi"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : 2lvqyyzt9, 3t1l09dyk, e0tdz7cvh"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  9"
## [1] "Total number of outliers motor task:  1"
## [1] "Total number of outliers perceptive task:  3"
## [1] "Total number of outliers logical task:  6"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   3151.9   3174.9  -1571.9   3143.9     2336 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9957 -0.8530 -0.5675  0.9526  2.1623 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.6875   0.8292  
## Number of obs: 2340, groups:  IDjoueur, 78
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1106     0.1603  -6.928 4.27e-12 ***
## difficulty    3.2297     0.3438   9.395  < 2e-16 ***
## timeNorm     -0.4618     0.1650  -2.798  0.00514 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.585       
## timeNorm   -0.216 -0.401
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      2340         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-2.126430  
##  1st Qu.:-0.347466  
##  Median : 0.049837  
##  Mean   : 0.003376  
##  3rd Qu.: 0.448296  
##  Max.   : 1.960052  
## [1] "Intercept: -1.11 4.3e-12 ***"
## [1] "Difficulty: 3.23 5.7e-21 ***"
## [1] "Time: -0.462 0.0051 **"
## [1] "R2 fixed: 0.13"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.61"
## [1] "AIC: 3200"
##          0%         25%         50%         75%        100% 
## -1.96005180 -0.44829605 -0.04983725  0.34746630  2.12642981

##          0%         25%         50%         75%        100% 
## -1.96005180 -0.44829605 -0.04983725  0.34746630  2.12642981

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2532.7   2555.3  -1262.3   2524.7     2126 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5031 -0.7242 -0.3428  0.8058  3.7576 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5808   0.7621  
## Number of obs: 2130, groups:  IDjoueur, 71
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.1935     0.1751 -12.529   <2e-16 ***
## difficulty    9.0967     0.5527  16.459   <2e-16 ***
## timeNorm     -0.3650     0.1824  -2.001   0.0454 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.615       
## timeNorm   -0.317 -0.299
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      2130 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.476587  
##  1st Qu.:-0.370451  
##  Median :-0.036883  
##  Mean   : 0.003092  
##  3rd Qu.: 0.449790  
##  Max.   : 1.689450  
## [1] "Intercept: -2.19 5.2e-36 ***"
## [1] "Difficulty: 9.1 7.2e-61 ***"
## [1] "Time: -0.365 0.045 *"
## [1] "R2 fixed: 0.31"
## [1] "R2 mixed: 0.42"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2500"
##          0%         25%         50%         75%        100% 
## -1.68944968 -0.44979001  0.03688316  0.37045061  1.47658650

##          0%         25%         50%         75%        100% 
## -1.68944968 -0.44979001  0.03688316  0.37045061  1.47658650

## `geom_smooth()` using method = 'gam'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2839.0   2861.8  -1415.5   2831.0     2216 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7599 -0.7933 -0.4388  0.8894  5.9937 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.538    1.24    
## Number of obs: 2220, groups:  IDjoueur, 74
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6820     0.1962  -8.574  < 2e-16 ***
## difficulty    4.8761     0.3902  12.497  < 2e-16 ***
## timeNorm     -1.0018     0.2129  -4.707 2.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.435       
## timeNorm   -0.095 -0.617
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      2220         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-2.40565  
##  1st Qu.:-0.91465  
##  Median :-0.21046  
##  Mean   : 0.00762  
##  3rd Qu.: 1.01654  
##  Max.   : 2.33338  
## [1] "Intercept: -1.68 1e-17 ***"
## [1] "Difficulty: 4.88 7.8e-36 ***"
## [1] "Time: -1 2.5e-06 ***"
## [1] "R2 fixed: 0.25"
## [1] "R2 mixed: 0.49"
## [1] "Cross Val: 0.65"
## [1] "AIC: 2800"
##         0%        25%        50%        75%       100% 
## -2.3333793 -1.0165435  0.2104629  0.9146509  2.4056548

##         0%        25%        50%        75%       100% 
## -2.3333793 -1.0165435  0.2104629  0.9146509  2.4056548

## `geom_smooth()` using method = 'gam'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

Playing board games in general and level for each task

Self efficacy and level for each task

## [1] "self.eff.on.level.m 0.26 0.0019 **"
## [1] "self.eff.on.level.l 0.17 0.048 *"

Risk aversion and level for each task

## [1] "risk.av.on.level.s 0.29 0.00094 ***"
## [1] "risk.av.on.level.l 0.28 0.00098 ***"

Age and level for each task

Sex and level for each task

## [1] "sexe.on.level.m -0.39 2.6e-05 ***"
## [1] "sexe.on.level.l -0.25 0.0098 **"
## [1] "sexe.on.level.m.2 -0.68 2.6e-05 *** mean(A): 0.2 mean(B): -0.56"
## [1] "sexe.on.level.l.2 -0.82 0.0099 ** mean(A): 0.2 mean(B): -0.54"

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

Playing board game in general and subjective difficulty error

In game level and subjective difficulty error

Sex and subjective difficulty error

Risk aversion and subjective difficulty error

Self efficacy and subjective difficulty error

OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 27   3e-04 ***
##  2:      0.09375        -0.0220 59     0.035 *
##  3:      0.15625        -0.0250 68     0.32 :(
##  4:      0.21875        -0.0045 72     0.75 :(
##  5:      0.28125        -0.0260 77     0.29 :(
##  6:      0.34375        -0.0290 80     0.23 :(
##  7:      0.40625        -0.0340 80     0.18 :(
##  8:      0.46875        -0.0750 80   0.0067 **
##  9:      0.53125        -0.1200 80 4.8e-06 ***
## 10:      0.59375        -0.1300 78 4.8e-06 ***
## 11:      0.65625        -0.2200 79 2.4e-09 ***
## 12:      0.71875        -0.2200 76 3.1e-09 ***
## 13:      0.78125        -0.2500 51   9e-08 ***
## 14:      0.84375        -0.2400 56 3.3e-06 ***
## 15:      0.90625        -0.1900 30 1.2e-06 ***
## 16:      0.96875        -0.2300 17 0.00023 ***

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125        -0.0310 22  0.003 **
##  2:      0.09375        -0.0045 30   0.93 :(
##  3:      0.15625         0.0045 32   0.86 :(
##  4:      0.21875         0.0210 35   0.65 :(
##  5:      0.28125         0.0480 34   0.16 :(
##  6:      0.34375        -0.0200 33   0.73 :(
##  7:      0.40625        -0.0430 34   0.22 :(
##  8:      0.46875        -0.0880 34   0.073 .
##  9:      0.53125        -0.1200 33   0.017 *
## 10:      0.59375        -0.1700 33 0.0027 **
## 11:      0.65625        -0.2100 32 0.0018 **
## 12:      0.71875        -0.2200 26 0.0034 **
## 13:      0.78125        -0.2100 14   0.032 *
## 14:      0.84375        -0.3100  9   0.024 *
## 15:      0.90625        -0.1900  6   0.036 *
## 16:      0.96875             NA  4        NA
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  7          NA
##  2:      0.09375         -0.094 44 0.00048 ***
##  3:      0.15625         -0.044 42     0.13 :(
##  4:      0.21875         -0.076 48     0.081 .
##  5:      0.28125         -0.067 53      0.05 .
##  6:      0.34375         -0.031 59     0.21 :(
##  7:      0.40625         -0.038 60     0.33 :(
##  8:      0.46875         -0.077 55     0.039 *
##  9:      0.53125         -0.120 57   0.0017 **
## 10:      0.59375         -0.150 55   0.0031 **
## 11:      0.65625         -0.230 55 2.1e-05 ***
## 12:      0.71875         -0.190 50 0.00014 ***
## 13:      0.78125         -0.210 33 4.1e-05 ***
## 14:      0.84375         -0.240 30 0.00025 ***
## 15:      0.90625         -0.160 17 0.00023 ***
## 16:      0.96875         -0.330  9    0.008 **
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  7          NA
##  3:      0.15625         -0.057 23     0.092 .
##  4:      0.21875         -0.076 21     0.25 :(
##  5:      0.28125         -0.067 35     0.084 .
##  6:      0.34375         -0.076 37     0.063 .
##  7:      0.40625         -0.061 39     0.13 :(
##  8:      0.46875         -0.120 40    0.006 **
##  9:      0.53125         -0.100 40   0.0086 **
## 10:      0.59375         -0.180 39   2e-04 ***
## 11:      0.65625         -0.230 36 1.5e-05 ***
## 12:      0.71875         -0.250 34 8.2e-05 ***
## 13:      0.78125         -0.280 19 0.00083 ***
## 14:      0.84375         -0.220 20     0.14 :(
## 15:      0.90625         -0.220  7      0.02 *
## 16:      0.96875         -0.160  4     0.098 .
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  3          NA
##  3:      0.15625        -0.0130 13     0.78 :(
##  4:      0.21875        -0.0045 29     0.21 :(
##  5:      0.28125        -0.0310 61      0.5 :(
##  6:      0.34375        -0.0720 73     0.025 *
##  7:      0.40625        -0.0810 74     0.025 *
##  8:      0.46875        -0.1000 74   0.0053 **
##  9:      0.53125        -0.1300 76 0.00052 ***
## 10:      0.59375        -0.2000 72 2.8e-06 ***
## 11:      0.65625        -0.2500 61 2.2e-06 ***
## 12:      0.71875        -0.2900 38 1.6e-05 ***
## 13:      0.78125        -0.4100 11   0.0065 **
## 14:      0.84375        -0.5400  4      0.2 :(
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  3      NA
##  3:      0.15625         -0.013 10 0.76 :(
##  4:      0.21875          0.019 18 0.79 :(
##  5:      0.28125          0.076 19 0.095 .
##  6:      0.34375          0.031 19 0.59 :(
##  7:      0.40625         -0.035 19 0.56 :(
##  8:      0.46875         -0.064 19 0.36 :(
##  9:      0.53125         -0.100 18 0.12 :(
## 10:      0.59375         -0.130 17 0.072 .
## 11:      0.65625         -0.220 12 0.054 .
## 12:      0.71875         -0.230  7 0.55 :(
## 13:      0.78125             NA  1      NA
## 14:      0.84375             NA  0      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  0          NA
##  3:      0.15625             NA  3          NA
##  4:      0.21875         -0.220 11      0.07 .
##  5:      0.28125         -0.055 33     0.63 :(
##  6:      0.34375         -0.068 38     0.13 :(
##  7:      0.40625         -0.094 38     0.051 .
##  8:      0.46875         -0.110 38     0.036 *
##  9:      0.53125         -0.120 38     0.045 *
## 10:      0.59375         -0.240 34   0.0013 **
## 11:      0.65625         -0.250 33   0.0016 **
## 12:      0.71875         -0.360 21 0.00068 ***
## 13:      0.78125         -0.410  6     0.031 *
## 14:      0.84375             NA  1          NA
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625             NA  0        NA
##  4:      0.21875             NA  0        NA
##  5:      0.28125         -0.210  9    0.02 *
##  6:      0.34375         -0.200 16 0.0059 **
##  7:      0.40625         -0.077 17   0.26 :(
##  8:      0.46875         -0.130 17   0.13 :(
##  9:      0.53125         -0.170 20   0.029 *
## 10:      0.59375         -0.240 21 0.0039 **
## 11:      0.65625         -0.330 16  0.004 **
## 12:      0.71875         -0.360 10   0.014 *
## 13:      0.78125         -0.570  4   0.095 .
## 14:      0.84375             NA  3        NA
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA 15          NA
##  2:      0.09375         -0.094 54 5.7e-07 ***
##  3:      0.15625         -0.095 61 1.7e-06 ***
##  4:      0.21875         -0.100 43 0.00062 ***
##  5:      0.28125         -0.094 51     0.017 *
##  6:      0.34375         -0.120 46   0.0058 **
##  7:      0.40625         -0.049 41     0.44 :(
##  8:      0.46875         -0.110 45   0.0057 **
##  9:      0.53125         -0.150 48 0.00038 ***
## 10:      0.59375         -0.079 37     0.094 .
## 11:      0.65625         -0.140 40   0.0036 **
## 12:      0.71875         -0.180 53 4.8e-05 ***
## 13:      0.78125         -0.140 33 0.00023 ***
## 14:      0.84375         -0.150 44   0.0018 **
## 15:      0.90625         -0.160 29 1.7e-06 ***
## 16:      0.96875         -0.230 17 0.00023 ***
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 9      NA
##  2:      0.09375         -0.094 9 0.13 :(
##  3:      0.15625             NA 9      NA
##  4:      0.21875         -0.160 5 0.054 .
##  5:      0.28125         -0.067 5 0.31 :(
##  6:      0.34375         -0.085 4 0.38 :(
##  7:      0.40625         -0.049 5    1 :(
##  8:      0.46875         -0.100 5 0.81 :(
##  9:      0.53125         -0.200 5 0.44 :(
## 10:      0.59375         -0.097 4 0.88 :(
## 11:      0.65625         -0.013 6    1 :(
## 12:      0.71875         -0.270 7 0.075 .
## 13:      0.78125         -0.085 4 0.58 :(
## 14:      0.84375         -0.200 6 0.14 :(
## 15:      0.90625         -0.190 6 0.036 *
## 16:      0.96875             NA 4      NA
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  6          NA
##  2:      0.09375         -0.094 38 2.2e-06 ***
##  3:      0.15625         -0.110 32 0.00024 ***
##  4:      0.21875         -0.150 24 0.00073 ***
##  5:      0.28125         -0.110 25     0.036 *
##  6:      0.34375         -0.094 27     0.055 .
##  7:      0.40625         -0.085 24     0.27 :(
##  8:      0.46875         -0.120 22     0.055 .
##  9:      0.53125         -0.180 27   0.0032 **
## 10:      0.59375         -0.130 22     0.038 *
## 11:      0.65625         -0.270 20 0.00072 ***
## 12:      0.71875         -0.170 26   0.0041 **
## 13:      0.78125         -0.210 21 0.00093 ***
## 14:      0.84375         -0.130 23   0.0046 **
## 15:      0.90625         -0.120 16 0.00033 ***
## 16:      0.96875         -0.330  9    0.008 **
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  7      NA
##  3:      0.15625        -0.0540 20 0.12 :(
##  4:      0.21875        -0.0044 14 0.95 :(
##  5:      0.28125        -0.0790 21 0.27 :(
##  6:      0.34375        -0.1300 15  0.1 :(
##  7:      0.40625         0.0220 12 0.56 :(
##  8:      0.46875        -0.1300 18 0.081 .
##  9:      0.53125        -0.0870 16 0.093 .
## 10:      0.59375         0.0130 11 0.89 :(
## 11:      0.65625        -0.0130 14 0.66 :(
## 12:      0.71875        -0.2000 20 0.038 *
## 13:      0.78125        -0.1400  8 0.23 :(
## 14:      0.84375        -0.1700 15 0.79 :(
## 15:      0.90625        -0.2200  7  0.02 *
## 16:      0.96875        -0.1600  4 0.098 .
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 17     0.027 *
##  2:      0.09375          0.025 38     0.69 :(
##  3:      0.15625          0.058 51     0.15 :(
##  4:      0.21875          0.067 61     0.17 :(
##  5:      0.28125          0.040 66     0.11 :(
##  6:      0.34375          0.034 71      0.4 :(
##  7:      0.40625          0.010 71     0.83 :(
##  8:      0.46875         -0.062 72     0.054 .
##  9:      0.53125         -0.091 73     0.014 *
## 10:      0.59375         -0.130 67   0.0016 **
## 11:      0.65625         -0.230 63 4.3e-07 ***
## 12:      0.71875         -0.210 52 1.7e-05 ***
## 13:      0.78125         -0.280 30 0.00012 ***
## 14:      0.84375         -0.420 14   0.0012 **
## 15:      0.90625             NA  1          NA
## 16:      0.96875             NA  0          NA
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125         -0.031 16    0.04 *
##  2:      0.09375          0.025 25   0.76 :(
##  3:      0.15625          0.130 25   0.23 :(
##  4:      0.21875          0.100 25   0.11 :(
##  5:      0.28125          0.040 24   0.47 :(
##  6:      0.34375         -0.058 25   0.48 :(
##  7:      0.40625         -0.049 24   0.38 :(
##  8:      0.46875         -0.110 25   0.021 *
##  9:      0.53125         -0.120 24   0.047 *
## 10:      0.59375         -0.170 23 0.0048 **
## 11:      0.65625         -0.230 22  0.002 **
## 12:      0.71875         -0.290 19  0.002 **
## 13:      0.78125         -0.280 10   0.041 *
## 14:      0.84375         -0.450  4   0.098 .
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  1      NA
##  2:      0.09375          0.013 13 0.83 :(
##  3:      0.15625          0.058 22 0.23 :(
##  4:      0.21875          0.140 24 0.083 .
##  5:      0.28125          0.058 24 0.14 :(
##  6:      0.34375          0.150 24 0.038 *
##  7:      0.40625          0.094 23 0.091 .
##  8:      0.46875          0.031 23  0.7 :(
##  9:      0.53125         -0.013 24 0.92 :(
## 10:      0.59375         -0.022 22 0.72 :(
## 11:      0.65625         -0.130 22 0.096 .
## 12:      0.71875         -0.040 19 0.36 :(
## 13:      0.78125         -0.170 11 0.067 .
## 14:      0.84375         -0.430  7 0.034 *
## 15:      0.90625             NA  1      NA
## 16:      0.96875             NA  0      NA
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  0          NA
##  3:      0.15625         -0.060  4     0.36 :(
##  4:      0.21875         -0.160 12     0.21 :(
##  5:      0.28125          0.033 18     0.51 :(
##  6:      0.34375         -0.010 22     0.82 :(
##  7:      0.40625         -0.013 24     0.68 :(
##  8:      0.46875         -0.088 24     0.079 .
##  9:      0.53125         -0.110 25     0.038 *
## 10:      0.59375         -0.200 22      0.01 *
## 11:      0.65625         -0.290 19 0.00023 ***
## 12:      0.71875         -0.290 14   0.0024 **
## 13:      0.78125         -0.350  9   0.0084 **
## 14:      0.84375             NA  3          NA
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.47512  -0.32099   0.00198   0.28034   0.62514  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17124    0.02662   6.432 1.52e-10 ***
## timeNorm     0.01183    0.02348   0.504    0.614    
## obj.diff    -0.63770    0.05194 -12.277  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1062912)
## 
##     Null deviance: 264.48  on 2339  degrees of freedom
## Residual deviance: 248.40  on 2337  degrees of freedom
## AIC: 1400.3
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73012  -0.23623  -0.06378   0.27512   0.77690  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03288    0.01832  -1.795   0.0728 .  
## timeNorm     0.05464    0.02474   2.209   0.0273 *  
## obj.diff    -0.23453    0.03151  -7.443 1.43e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1028656)
## 
##     Null deviance: 224.52  on 2129  degrees of freedom
## Residual deviance: 218.80  on 2127  degrees of freedom
## AIC: 1205.3
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.57929  -0.31805  -0.00183   0.31692   0.63841  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17798    0.01962   9.072  < 2e-16 ***
## timeNorm     0.07804    0.02588   3.016  0.00259 ** 
## obj.diff    -0.60382    0.04194 -14.398  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1105958)
## 
##     Null deviance: 268.47  on 2219  degrees of freedom
## Residual deviance: 245.19  on 2217  degrees of freedom
## AIC: 1416.9
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2887668     0.4250930 -0.13953917 234 8.3e-09 ***
##  2:      4.5      0.3577534     0.4490995 -0.09225023 234 0.00012 ***
##  3:      7.5      0.3736264     0.4588281 -0.08415455 234   0.0011 **
##  4:     10.5      0.3443223     0.4590774 -0.11697149 234 1.4e-06 ***
##  5:     13.5      0.3388278     0.4603425 -0.12311547 234 1.6e-07 ***
##  6:     16.5      0.3504274     0.4656950 -0.11694812 234 1.1e-06 ***
##  7:     19.5      0.3284493     0.4694363 -0.14111674 234 1.2e-09 ***
##  8:     22.5      0.3431013     0.4793797 -0.14609825 234 4.7e-09 ***
##  9:     25.5      0.3833944     0.4792794 -0.09957072 234 0.00013 ***
## 10:     28.5      0.3363858     0.4675171 -0.12098146 234 4.9e-08 ***
##     time  error.diff shapes
##  1:  1.5 -0.13953917     24
##  2:  4.5 -0.09225023     24
##  3:  7.5 -0.08415455     24
##  4: 10.5 -0.11697149     24
##  5: 13.5 -0.12311547     24
##  6: 16.5 -0.11694812     24
##  7: 19.5 -0.14111674     24
##  8: 22.5 -0.14609825     24
##  9: 25.5 -0.09957072     24
## 10: 28.5 -0.12098146     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1368209     0.2153795 -0.10692408 213 3.2e-08 ***
##  2:      4.5      0.2964453     0.4306730 -0.15011494 213   3e-09 ***
##  3:      7.5      0.3333333     0.4960787 -0.16696074 213 9.6e-11 ***
##  4:     10.5      0.3521127     0.4942637 -0.14997408 213 5.9e-09 ***
##  5:     13.5      0.3876593     0.4785235 -0.09232594 213   1e-04 ***
##  6:     16.5      0.3970490     0.4910775 -0.09082835 213 0.00014 ***
##  7:     19.5      0.3816231     0.4794597 -0.10563208 213 1.2e-07 ***
##  8:     22.5      0.4084507     0.5148548 -0.10674468 213 8.5e-05 ***
##  9:     25.5      0.4151576     0.4815308 -0.06349500 213     0.011 *
## 10:     28.5      0.3474178     0.4938488 -0.15316819 213 1.4e-08 ***
##     time  error.diff shapes
##  1:  1.5 -0.10692408     24
##  2:  4.5 -0.15011494     24
##  3:  7.5 -0.16696074     24
##  4: 10.5 -0.14997408     24
##  5: 13.5 -0.09232594     24
##  6: 16.5 -0.09082835     24
##  7: 19.5 -0.10563208     24
##  8: 22.5 -0.10674468     24
##  9: 25.5 -0.06349500     24
## 10: 28.5 -0.15316819     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.2149292     0.2543626 -0.07057268 222 0.0012 **
##  2:      4.5      0.3558559     0.3238165  0.03016309 222   0.24 :(
##  3:      7.5      0.3873874     0.4029150 -0.01604677 222   0.54 :(
##  4:     10.5      0.3925354     0.4338872 -0.03958987 222    0.1 :(
##  5:     13.5      0.4118404     0.4496167 -0.04062829 222   0.11 :(
##  6:     16.5      0.4227799     0.4766409 -0.05128067 222   0.032 *
##  7:     19.5      0.4111969     0.4797636 -0.07613895 222 0.0041 **
##  8:     22.5      0.3880309     0.4387192 -0.05567027 222   0.025 *
##  9:     25.5      0.4182754     0.4588002 -0.04151817 222   0.098 .
## 10:     28.5      0.4427284     0.4780249 -0.03297758 222   0.24 :(
##     time  error.diff shapes
##  1:  1.5 -0.07057268     24
##  2:  4.5  0.03016309     16
##  3:  7.5 -0.01604677     16
##  4: 10.5 -0.03958987     16
##  5: 13.5 -0.04062829     16
##  6: 16.5 -0.05128067     24
##  7: 19.5 -0.07613895     24
##  8: 22.5 -0.05567027     24
##  9: 25.5 -0.04151817     16
## 10: 28.5 -0.03297758     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.62236  -0.30061  -0.03105   0.30943   0.70258  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07333    0.02692   2.724   0.0065 ** 
## timeNorm     0.04162    0.02533   1.643   0.1005    
## obj.diff    -0.45089    0.04604  -9.794   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1116794)
## 
##     Null deviance: 245.47  on 2099  degrees of freedom
## Residual deviance: 234.19  on 2097  degrees of freedom
## AIC: 1361.1
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66633  -0.30168  -0.03745   0.30093   0.70159  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05934    0.01808   3.282 0.001043 ** 
## timeNorm     0.07874    0.02174   3.622 0.000297 ***
## obj.diff    -0.40644    0.03402 -11.948  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1130013)
## 
##     Null deviance: 354.95  on 2999  degrees of freedom
## Residual deviance: 338.66  on 2997  degrees of freedom
## AIC: 1977.6
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61482  -0.25581  -0.02273   0.26062   0.68726  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12404    0.01869   6.637 4.39e-11 ***
## timeNorm    -0.03577    0.03222  -1.110    0.267    
## obj.diff    -0.40591    0.04638  -8.753  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09536639)
## 
##     Null deviance: 163.52  on 1589  degrees of freedom
## Residual deviance: 151.35  on 1587  degrees of freedom
## AIC: 780.68
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2836735     0.4554798 -0.18122716 210 5.2e-11 ***
##  2:      4.5      0.3761905     0.4871346 -0.11327679 210 1.9e-05 ***
##  3:      7.5      0.3857143     0.5001913 -0.11477171 210 1.8e-05 ***
##  4:     10.5      0.3367347     0.4808474 -0.14718789 210 9.8e-09 ***
##  5:     13.5      0.3693878     0.4743164 -0.10640922 210   5e-05 ***
##  6:     16.5      0.3693878     0.4800480 -0.11073776 210 4.5e-06 ***
##  7:     19.5      0.3197279     0.4553800 -0.14906689 210 8.6e-08 ***
##  8:     22.5      0.3591837     0.4524433 -0.09927856 210 0.00016 ***
##  9:     25.5      0.3598639     0.4321020 -0.07754019 210   0.0036 **
## 10:     28.5      0.3598639     0.4649090 -0.10777513 210 5.4e-05 ***
##     time  error.diff shapes
##  1:  1.5 -0.18122716     24
##  2:  4.5 -0.11327679     24
##  3:  7.5 -0.11477171     24
##  4: 10.5 -0.14718789     24
##  5: 13.5 -0.10640922     24
##  6: 16.5 -0.11073776     24
##  7: 19.5 -0.14906689     24
##  8: 22.5 -0.09927856     24
##  9: 25.5 -0.07754019     24
## 10: 28.5 -0.10777513     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2123810     0.2820147 -0.09988244 300 4.7e-07 ***
##  2:      4.5      0.3442857     0.4311776 -0.09488366 300 1.4e-05 ***
##  3:      7.5      0.3504762     0.4731184 -0.12644513 300 5.8e-08 ***
##  4:     10.5      0.3680952     0.4748829 -0.11135073 300 9.7e-07 ***
##  5:     13.5      0.3747619     0.4484668 -0.07739952 300 0.00028 ***
##  6:     16.5      0.3961905     0.4665071 -0.06789986 300   0.0013 **
##  7:     19.5      0.4095238     0.4825212 -0.07963820 300 8.8e-05 ***
##  8:     22.5      0.3904762     0.4837553 -0.09664082 300 1.4e-05 ***
##  9:     25.5      0.4385714     0.4854995 -0.04906552 300     0.035 *
## 10:     28.5      0.3847619     0.4690868 -0.08176457 300 5.3e-05 ***
##     time  error.diff shapes
##  1:  1.5 -0.09988244     24
##  2:  4.5 -0.09488366     24
##  3:  7.5 -0.12644513     24
##  4: 10.5 -0.11135073     24
##  5: 13.5 -0.07739952     24
##  6: 16.5 -0.06789986     24
##  7: 19.5 -0.07963820     24
##  8: 22.5 -0.09664082     24
##  9: 25.5 -0.04906552     24
## 10: 28.5 -0.08176457     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1329739     0.1356034 -0.04799946 159   0.0086 **
##  2:      4.5      0.2740341     0.2330713  0.02295544 159     0.35 :(
##  3:      7.5      0.3665768     0.3490692  0.01576858 159     0.59 :(
##  4:     10.5      0.3872417     0.4124680 -0.02740123 159     0.31 :(
##  5:     13.5      0.3980234     0.4736736 -0.08042557 159   0.0021 **
##  6:     16.5      0.4025157     0.4944921 -0.08989010 159   0.0011 **
##  7:     19.5      0.3737646     0.4911595 -0.11967220 159 1.8e-05 ***
##  8:     22.5      0.3827493     0.4974522 -0.11798555 159   8e-05 ***
##  9:     25.5      0.4016173     0.5042757 -0.10492584 159 0.00099 ***
## 10:     28.5      0.3773585     0.5179459 -0.14347107 159 1.5e-05 ***
##     time  error.diff shapes
##  1:  1.5 -0.04799946     24
##  2:  4.5  0.02295544     16
##  3:  7.5  0.01576858     16
##  4: 10.5 -0.02740123     16
##  5: 13.5 -0.08042557     24
##  6: 16.5 -0.08989010     24
##  7: 19.5 -0.11967220     24
##  8: 22.5 -0.11798555     24
##  9: 25.5 -0.10492584     24
## 10: 28.5 -0.14347107     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4018  -0.3136  -0.0293   0.2819   0.5717  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.16190    0.07389   2.191   0.0288 *  
## timeNorm     0.04169    0.04874   0.855   0.3927    
## obj.diff    -0.71660    0.12152  -5.897 6.05e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1121478)
## 
##     Null deviance: 75.138  on 629  degrees of freedom
## Residual deviance: 70.317  on 627  degrees of freedom
## AIC: 414.45
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5      0.2879819     0.6028422 -0.3063430 63 2.4e-07 ***
##  2:      4.5      0.3560091     0.5311773 -0.1771179 63 0.00091 ***
##  3:      7.5      0.3514739     0.5237554 -0.1811341 63    0.003 **
##  4:     10.5      0.3219955     0.5051186 -0.1823421 63 0.00032 ***
##  5:     13.5      0.3492063     0.4887080 -0.1436574 63   0.0023 **
##  6:     16.5      0.3106576     0.4986379 -0.1967927 63 5.4e-05 ***
##  7:     19.5      0.2585034     0.4553692 -0.2043180 63 7.7e-05 ***
##  8:     22.5      0.3242630     0.4520935 -0.1492351 63   0.0034 **
##  9:     25.5      0.3446712     0.4503762 -0.1189400 63     0.025 *
## 10:     28.5      0.3446712     0.4851210 -0.1361192 63   0.0034 **
##     time error.diff shapes
##  1:  1.5 -0.3063430     24
##  2:  4.5 -0.1771179     24
##  3:  7.5 -0.1811341     24
##  4: 10.5 -0.1823421     24
##  5: 13.5 -0.1436574     24
##  6: 16.5 -0.1967927     24
##  7: 19.5 -0.2043180     24
##  8: 22.5 -0.1492351     24
##  9: 25.5 -0.1189400     24
## 10: 28.5 -0.1361192     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5003  -0.3105  -0.0105   0.2884   0.6198  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10023    0.04183   2.396   0.0167 *  
## timeNorm     0.03586    0.03385   1.059   0.2896    
## obj.diff    -0.51350    0.07991  -6.426 1.92e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1086791)
## 
##     Null deviance: 128.16  on 1139  degrees of freedom
## Residual deviance: 123.57  on 1137  degrees of freedom
## AIC: 710.11
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3182957     0.4221507 -0.11161746 114 0.00051 ***
##  2:      4.5      0.3496241     0.4741037 -0.12734971 114 7.7e-05 ***
##  3:      7.5      0.3558897     0.4724383 -0.12056326 114 0.00085 ***
##  4:     10.5      0.3245614     0.4742501 -0.15291189 114 6.8e-06 ***
##  5:     13.5      0.3258145     0.4784144 -0.15372301 114 9.2e-06 ***
##  6:     16.5      0.3383459     0.4678202 -0.12867560 114   2e-04 ***
##  7:     19.5      0.3446115     0.4569733 -0.11111363 114 0.00033 ***
##  8:     22.5      0.3320802     0.4659744 -0.14627777 114   5e-05 ***
##  9:     25.5      0.3997494     0.4753022 -0.08100621 114     0.035 *
## 10:     28.5      0.3508772     0.4420746 -0.08130956 114    0.003 **
##     time  error.diff shapes
##  1:  1.5 -0.11161746     24
##  2:  4.5 -0.12734971     24
##  3:  7.5 -0.12056326     24
##  4: 10.5 -0.15291189     24
##  5: 13.5 -0.15372301     24
##  6: 16.5 -0.12867560     24
##  7: 19.5 -0.11111363     24
##  8: 22.5 -0.14627777     24
##  9: 25.5 -0.08100621     24
## 10: 28.5 -0.08130956     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5243  -0.2887   0.0170   0.2472   0.6074  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19432    0.03803   5.110 4.42e-07 ***
## timeNorm    -0.16087    0.05554  -2.897 0.003919 ** 
## obj.diff    -0.38512    0.10925  -3.525 0.000458 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09099729)
## 
##     Null deviance: 56.480  on 569  degrees of freedom
## Residual deviance: 51.595  on 567  degrees of freedom
## AIC: 256.33
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.2305764     0.2345181 -0.022364755 57     0.59 :(
##  2:      4.5      0.3759398     0.3083734  0.059567775 57     0.083 .
##  3:      7.5      0.4335840     0.3598460  0.088313450 57     0.068 .
##  4:     10.5      0.4085213     0.3778445  0.026989430 57     0.57 :(
##  5:     13.5      0.3533835     0.3928473 -0.037906228 57     0.35 :(
##  6:     16.5      0.4185464     0.4250343 -0.003127012 57     0.94 :(
##  7:     19.5      0.3734336     0.5099102 -0.128686755 57   0.0067 **
##  8:     22.5      0.3859649     0.5363487 -0.151942393 57   0.0033 **
##  9:     25.5      0.3934837     0.5191794 -0.124405638 57     0.013 *
## 10:     28.5      0.2982456     0.4989451 -0.208899524 57 0.00012 ***
##     time   error.diff shapes
##  1:  1.5 -0.022364755     16
##  2:  4.5  0.059567775     16
##  3:  7.5  0.088313450     16
##  4: 10.5  0.026989430     16
##  5: 13.5 -0.037906228     16
##  6: 16.5 -0.003127012     16
##  7: 19.5 -0.128686755     24
##  8: 22.5 -0.151942393     24
##  9: 25.5 -0.124405638     24
## 10: 28.5 -0.208899524     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7257  -0.2678  -0.1262   0.3162   0.7323  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01988    0.03634   0.547    0.585    
## timeNorm     0.04587    0.04484   1.023    0.307    
## obj.diff    -0.28844    0.06270  -4.600 4.99e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1187679)
## 
##     Null deviance: 87.696  on 719  degrees of freedom
## Residual deviance: 85.157  on 717  degrees of freedom
## AIC: 514.24
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2281746     0.3097399 -0.10902976 72   0.038 *
##  2:      4.5      0.3630952     0.4426004 -0.08561359 72   0.053 .
##  3:      7.5      0.3630952     0.4724575 -0.11685188 72 0.0061 **
##  4:     10.5      0.3392857     0.4525641 -0.12298405 72 0.0052 **
##  5:     13.5      0.3670635     0.4656216 -0.09769915 72   0.047 *
##  6:     16.5      0.4087302     0.4811146 -0.06977024 72   0.061 .
##  7:     19.5      0.3392857     0.4360550 -0.11830674 72   0.011 *
##  8:     22.5      0.4365079     0.4764586 -0.03047354 72   0.52 :(
##  9:     25.5      0.3769841     0.4251328 -0.04729018 72   0.27 :(
## 10:     28.5      0.3710317     0.4756434 -0.11448523 72   0.021 *
##     time  error.diff shapes
##  1:  1.5 -0.10902976     24
##  2:  4.5 -0.08561359     16
##  3:  7.5 -0.11685188     24
##  4: 10.5 -0.12298405     24
##  5: 13.5 -0.09769915     24
##  6: 16.5 -0.06977024     16
##  7: 19.5 -0.11830674     24
##  8: 22.5 -0.03047354     16
##  9: 25.5 -0.04729018     16
## 10: 28.5 -0.11448523     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70507  -0.22068  -0.04335   0.24408   0.76625  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.06325    0.02388  -2.648  0.00820 ** 
## timeNorm     0.08543    0.03269   2.613  0.00909 ** 
## obj.diff    -0.23906    0.04135  -5.782 9.53e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09501969)
## 
##     Null deviance: 111.35  on 1139  degrees of freedom
## Residual deviance: 108.04  on 1137  degrees of freedom
## AIC: 556.99
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5     0.09899749     0.1876252 -0.11039497 114 1.2e-07 ***
##  2:      4.5     0.28446115     0.4616587 -0.18406741 114 2.4e-08 ***
##  3:      7.5     0.30325815     0.5145617 -0.21299544 114 2.4e-09 ***
##  4:     10.5     0.33583960     0.4960885 -0.16726069 114   2e-06 ***
##  5:     13.5     0.34085213     0.4349206 -0.09783900 114 0.00087 ***
##  6:     16.5     0.37468672     0.4700852 -0.09179220 114   0.0085 **
##  7:     19.5     0.41102757     0.5126837 -0.10061540 114 4.9e-06 ***
##  8:     22.5     0.39097744     0.5324474 -0.14712519 114 0.00012 ***
##  9:     25.5     0.44987469     0.5092968 -0.05935521 114      0.1 :(
## 10:     28.5     0.32957393     0.4942599 -0.17055244 114 9.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.11039497     24
##  2:  4.5 -0.18406741     24
##  3:  7.5 -0.21299544     24
##  4: 10.5 -0.16726069     24
##  5: 13.5 -0.09783900     24
##  6: 16.5 -0.09179220     24
##  7: 19.5 -0.10061540     24
##  8: 22.5 -0.14712519     24
##  9: 25.5 -0.05935521     16
## 10: 28.5 -0.17055244     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70375  -0.19717  -0.02273   0.23861   0.72796  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02461    0.04370  -0.563    0.574
## timeNorm    -0.08556    0.06838  -1.251    0.212
## obj.diff    -0.08977    0.07434  -1.208    0.228
## 
## (Dispersion parameter for gaussian family taken to be 0.09043054)
## 
##     Null deviance: 24.581  on 269  degrees of freedom
## Residual deviance: 24.145  on 267  degrees of freedom
## AIC: 122.35
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5     0.05291005    0.08093653 -0.05322518 27 0.0059 **
##  2:      4.5     0.16931217    0.26803793 -0.14051200 27   0.036 *
##  3:      7.5     0.38095238    0.48102915 -0.09893471 27   0.19 :(
##  4:     10.5     0.45502646    0.59775767 -0.15065745 27   0.052 .
##  5:     13.5     0.64021164    0.69702994 -0.05290538 27   0.32 :(
##  6:     16.5     0.46031746    0.60627919 -0.13542918 27 0.0082 **
##  7:     19.5     0.37037037    0.45492616 -0.09534505 27   0.16 :(
##  8:     22.5     0.40740741    0.54296476 -0.10429981 27   0.014 *
##  9:     25.5     0.37037037    0.51469153 -0.14376601 27   0.046 *
## 10:     28.5     0.35978836    0.54066115 -0.19060030 27   0.028 *
##     time  error.diff shapes
##  1:  1.5 -0.05322518     24
##  2:  4.5 -0.14051200     24
##  3:  7.5 -0.09893471     16
##  4: 10.5 -0.15065745     16
##  5: 13.5 -0.05290538     16
##  6: 16.5 -0.13542918     24
##  7: 19.5 -0.09534505     16
##  8: 22.5 -0.10429981     24
##  9: 25.5 -0.14376601     24
## 10: 28.5 -0.19060030     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.50249  -0.31192   0.00158   0.27300   0.57238  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18310    0.05115   3.580 0.000366 ***
## timeNorm    -0.01421    0.04068  -0.349 0.726902    
## obj.diff    -0.58809    0.08998  -6.536 1.17e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1006514)
## 
##     Null deviance: 79.537  on 749  degrees of freedom
## Residual deviance: 75.187  on 747  degrees of freedom
## AIC: 411.33
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3333333     0.4716057 -0.14568003 75 0.00049 ***
##  2:      4.5      0.4057143     0.4928915 -0.09166913 75     0.041 *
##  3:      7.5      0.4361905     0.5070220 -0.06921125 75      0.1 :(
##  4:     10.5      0.3466667     0.4876115 -0.14592638 75 0.00062 ***
##  5:     13.5      0.3885714     0.4705744 -0.08546184 75     0.042 *
##  6:     16.5      0.3809524     0.4634084 -0.08072180 75     0.037 *
##  7:     19.5      0.3523810     0.4739411 -0.13894639 75   0.0027 **
##  8:     22.5      0.3142857     0.4296825 -0.12873142 75   0.0014 **
##  9:     25.5      0.3561905     0.4234421 -0.07931452 75     0.063 .
## 10:     28.5      0.3619048     0.4376259 -0.07920488 75     0.068 .
##     time  error.diff shapes
##  1:  1.5 -0.14568003     24
##  2:  4.5 -0.09166913     24
##  3:  7.5 -0.06921125     16
##  4: 10.5 -0.14592638     24
##  5: 13.5 -0.08546184     24
##  6: 16.5 -0.08072180     24
##  7: 19.5 -0.13894639     24
##  8: 22.5 -0.12873142     24
##  9: 25.5 -0.07931452     16
## 10: 28.5 -0.07920488     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6842  -0.3690   0.0772   0.3315   0.5546  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22719    0.03565   6.373 3.31e-10 ***
## timeNorm     0.12666    0.04933   2.568   0.0104 *  
## obj.diff    -0.59514    0.07771  -7.659 6.09e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1277274)
## 
##     Null deviance: 99.075  on 719  degrees of freedom
## Residual deviance: 91.581  on 717  degrees of freedom
## AIC: 566.61
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2242063     0.2095827 -0.02888658 72   0.68 :(
##  2:      4.5      0.4305556     0.3149495  0.11381852 72 0.0088 **
##  3:      7.5      0.4166667     0.4085764  0.01184582 72   0.84 :(
##  4:     10.5      0.4880952     0.4423093  0.05303055 72   0.34 :(
##  5:     13.5      0.5059524     0.4224978  0.09425537 72   0.053 .
##  6:     16.5      0.5218254     0.4587626  0.05950609 72   0.14 :(
##  7:     19.5      0.5099206     0.4752146  0.04435498 72   0.43 :(
##  8:     22.5      0.4821429     0.4348125  0.04340068 72   0.23 :(
##  9:     25.5      0.4821429     0.4639661  0.02021695 72   0.66 :(
## 10:     28.5      0.5257937     0.4719986  0.07500839 72   0.15 :(
##     time  error.diff shapes
##  1:  1.5 -0.02888658     16
##  2:  4.5  0.11381852     24
##  3:  7.5  0.01184582     16
##  4: 10.5  0.05303055     16
##  5: 13.5  0.09425537     16
##  6: 16.5  0.05950609     16
##  7: 19.5  0.04435498     16
##  8: 22.5  0.04340068     16
##  9: 25.5  0.02021695     16
## 10: 28.5  0.07500839     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.52214  -0.26501  -0.02093   0.25379   0.68368  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13267    0.02547   5.209 2.46e-07 ***
## timeNorm     0.17621    0.05107   3.451  0.00059 ***
## obj.diff    -0.72141    0.07276  -9.915  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09487992)
## 
##     Null deviance: 81.624  on 749  degrees of freedom
## Residual deviance: 70.875  on 747  degrees of freedom
## AIC: 367.04
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5     0.08761905    0.08010825 -0.052761161 75   0.023 *
##  2:      4.5     0.23428571    0.16325372  0.044004349 75   0.22 :(
##  3:      7.5     0.31047619    0.29337315  0.004158512 75   0.93 :(
##  4:     10.5     0.34666667    0.37207770 -0.026222602 75   0.49 :(
##  5:     13.5     0.34476190    0.45469333 -0.114025675 75 0.0029 **
##  6:     16.5     0.36952381    0.50703661 -0.142309890 75 0.0015 **
##  7:     19.5     0.37523810    0.48995306 -0.127835397 75 0.0033 **
##  8:     22.5     0.37142857    0.45150631 -0.089772558 75   0.072 .
##  9:     25.5     0.41904762    0.48919916 -0.072377500 75   0.15 :(
## 10:     28.5     0.44380952    0.52420913 -0.082245022 75   0.15 :(
##     time   error.diff shapes
##  1:  1.5 -0.052761161     24
##  2:  4.5  0.044004349     16
##  3:  7.5  0.004158512     16
##  4: 10.5 -0.026222602     16
##  5: 13.5 -0.114025675     24
##  6: 16.5 -0.142309890     24
##  7: 19.5 -0.127835397     24
##  8: 22.5 -0.089772558     16
##  9: 25.5 -0.072377500     16
## 10: 28.5 -0.082245022     16